Subarctic soil carbon losses after deforestation for agriculture depend on permafrost abundance
Bibliographic record
Abstract
The northern circumpolar permafrost region is experiencing considerable warming due to climate change, which is allowing agricultural production to expand into regions of discontinuous and continuous permafrost. The conversion of forests to arable land might further enhance permafrost thaw and affect soil organic carbon (SOC) that had previously been protected by frozen ground. The interactive effect of permafrost abundance and deforestation on SOC stocks has hardly been studied. In this study, soils were sampled on 18 farms across the Yukon on permafrost and non-permafrost soils to quantify the impact of land-use change from forest to cropland and grassland on SOC stocks. Furthermore, the soils were physically and chemically fractionated to assess the impact of land-use change on different functional pools of SOC. On average, permafrost-affected forest soils lost 15.6 ± 21.3% of SOC when converted to cropland and 23.0 ± 13.0% when converted to grassland. No permafrost was detected in the deforested soils, indicating that land-use change strongly enhanced warming and subsequent thawing. In contrast, the change in SOC at sites without permafrost was not significant but had a slight tendency to be positive. SOC stocks were generally lower at sites without permafrost under forest. Furthermore, land-use change increased mineral-associated SOC, while the fate of particulate organic matter (POM) after land-use change depended on permafrost occurrence. Permafrost soils showed significant POM losses after land-use change, while grassland sites without permafrost gained POM in the topsoil. The results showed that the fate of SOC after land-use change greatly depended on the abundance of permafrost in the pristine forest, which was driven by climatic conditions more than by soil properties. It can be concluded that in regions of discontinuous permafrost in particular, initial conditions in forest soils should be considered before deforestation to minimize its climate impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".